Approximation theory is that area of analysis which is concerned with the ability to approximate functions by simpler and more easily calculated functions. It is an area which, like many other fields of analysis, has its primary roots in the mathematics.The need for function approximation and classification arises in many branches of applied mathematics, computer science and data mining in particular.

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

Pervasive computing (also referred to as ubiquitous computing or ambient intelligence) aims to create environments where computers are invisibly and seamlessly integrated and connected into our everyday environment. Pervasive computing and intelligent multimedia technologies are becoming increasingly important, although many potential applications have not yet been fully realized. These key technologies are creating a multimedia revolution that will have significant impact across a wide spectrum of consumer, business, healthcare, and governmental domains.

Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning. Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its unique coverage of the defining issues of the field, this commanding research collection provides libraries with a single, authoritative reference to this highly advanced technological topic.

For the last decades Multimedia processing has emerged as an important technology to generate content based on images, video, audio, graphics, and text. Furthermore, the recent new development represented by High Definition Multimedia content and Interactive television will generate a huge volume of data and important computing problems connected with the creation, processing and management of Multimedia content. "Computational Intelligence in Multimedia Processing: Recent Advances" is a compilation of the latest trends and developments in the field of computational intelligence in multimedia processing. This edited book presents a large number of interesting applications to intelligent multimedia processing of various Computational Intelligence techniques, such as rough sets, Neural Networks; Fuzzy Logic; Evolutionary Computing; Artificial Immune Systems; Swarm Intelligence; Reinforcement Learning and evolutionary computation.

The purpose of this book is to provide a medium for an exchange of expertise and concerns. In order to achieve the goal, the editors have solicited contributions from both computational intelligence as well as biology researchers. They have collected contributions from the CI community describing powerful new methodologies that could, or currently are, utilized for biology-oriented applications. On the other hand, the book also contains chapters devoted to open problems in biology that are in need of strong computational techniques, so the CI community can find a brand new and potentially intriguing spectrum of applications.

The purpose of this book is to provide an overview of powerful state-of-the-art methodologies that are currently utilized for biomedicine and/ or bioinformatics-oriented applications, so that researchers working in those fields could learn of new methods to help them tackle their problems. On the other hand, the CI community will find this book useful by discovering a new and intriguing area of applications. In order to help fill the gap between the scientists on both sides of this spectrum, the editors have solicited contributions from researchers actively applying computational intelligence techniques to important problems in biomedicine and bioinformatics.

A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research. The contributors also cover state-of-the-art research toward integrating medical image processing with artificial intelligence and machine learning approaches.

High Internet penetration in regions such as North America, Australia, and Europe, has proven the World Wide Web as an important medium for e-commerce transaction. Despite the soaring adoption statistics for those already developed societies, diffusion rates still remain low for the less developed countries, with e-commerce in its infancy.Emerging Markets and E-Commerce in Developing Economies enhances understanding of e-commerce models and practices in less developed countries, and extends the growing literature on e-commerce. An essential addition to worldwide library collections in technology, commerce, social sciences, and related fields, this essential contribution expands the body of knowledge in the field with relevant theoretical foundations, methodologies, and frameworks, to the benefit of the international academic, research, governmental, and industrial communities.

Virtual reality technology enables people to become immersed in a computer-simulated and three-dimensional environment. In this chapter, we investigate the effects of the virtual reality technology on disabled people such as blind and visually impaired people (VIP) in order to enhance their computer skills and prepare them to make use of recent technology in their daily life. As well as, they need to advance their information technology skills beyond the basic computer training and skills. This chapter describes what best tools and practices in information technology to support disabled people such as deaf-blind and visual impaired people in their activities such as mobility systems, computer games, accessibility of e-learning, web-based information system, and wearable finger-braille interface for navigation of deaf-blind. Moreover, we will show how physical disabled people can benefits from the innovative virtual reality techniques and discuss some representative examples to illustrate how virtual reality technology can be utilized to address the information technology problem of blind and visual impaired people. Challenges to be addressed and an extensive bibliography are included.

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this chapter, we present a machine learning scheme, employing a combination of fuzzy sets, wavelets and rough sets, for analyzing prostrate ultrasound images in order diagnose prostate cancer. To address the image noise problem we first utilize an algorithm based on type-II fuzzy sets to enhance the contrast of the ultrasound image. This is followed by performing a modified fuzzy c-mean clustering algorithm in order to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalized, followed by application of a rough set analysis for discrimination of different regions of interest to determine whether they represent cancer or not. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including decision trees, discriminant analysis, rough neural networks, and neural networks.

Pulse-coupled neural networks (PCNNs) are a biologically inspired type of neural networks. It is a simplified model of the cat's visual cortex with local connections to other neurons. PCNN has the ability to extract edges, segments and texture information from images. Only a few changes to the PCNN parameters are necessary for effective operation on different types of data. This is an advantage over published image processing algorithms that generally require information about the target before they are effective. The main aim of this paper is to provide an accurate boundary detection algorithm of the prostate ultrasound images to assist radiologists in making their decisions. To increase the contrast of the ultrasound prostate image, the intensity values of the original images were adjusted firstly using the PCNN with median filter. It is followed by the PCNN segmentation algorithm to detect the boundary of the image. Combining adjusting and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. The experimental results obtained show that the overall boundary detection overlap accuracy offered by the employed PCNN approach is high compared with other machine learning techniques including Fuzzy C-mean and Fuzzy Type-II.

The objective of this book chapter is to present the rough sets and pulse coupled neural network scheme for Ultrasound Biomicroscopy glaucoma images analysis. To increase the efficiency of the introduced scheme, an intensity adjustment process is applied first using the Pulse Coupled Neural Network (PCNN) with a median filter. This is followed by applying the PCNN-based segmentation algorithm to detect the boundary of the interior chamber of the eye image. Then, glaucoma clinical parameters have been calculated and normalized, followed by application of a rough set analysis to discover the dependency between the parameters and to generate set of reduct that contains minimal number of attributes. Finally, a rough confusion matrix is designed for discrimination to test whether they are normal or glaucomatous eyes. Experimental results show that the introduced scheme is very successful and has high detection accuracy.

Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics, in marketing research, software engineering and management. This chapter discusses the state-of-the-art methodology for some mining time series databases and presents a new evolutionary algorithm for times series clustering an input time series data set. The data mining methods presented include techniques for efficient segmentation, indexing, and clustering time series.

Computational social science is a new emerging field that has overlapping regions from Mathematics, Psychology, Computer Sciences, Sociology,and Management. Social computing is concerned with the intersection of social behavior and computational systems. It supports any sort of social behavior in or through computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. Thus, blogs, email, instant messaging, social network services, wikis, social bookmarking, and other instances of what is often called social software illustrate ideas from social computing. Social network analysis is the study of relationships among social entities. It is becoming an important tool for investigators. However all the necessary information is often distributed over a number of Web sites. Interest in this field is blossoming as traditional practitioners in the social and behavioral sciences are being joined by researchers from statistics, graph theory, machine learning and data mining. In this chapter, we illustrate the concept of social networks from a computational point of view, with a focus on practical services, tools, and applications and open avenues for further research. Challenges to be addressed and future directions of research are presented and an extensive bibliography is also included.

The continuous self-growing nature of social networks makes it hard to define a line of safety around these networks. Users in social networks are not interacting with the web only, but also with trusted groups that may contain enemies. There are different kinds of attacks on these networks including causing damage to the computer systems and steeling information about users. These attacks are not affecting individuals only, but also the organizations they are belonging to. Protection from these attacks should be performed by the users and security experts of the network. Advices should be provided to users of these social networks. Also security-experts should be sure that the contents transmitted through the network do not contain malicious or harmful data. This chapter shows the security risks and the tasks applied to minimize those risks. Explain the most famous ways that attackers and malicious use. Then show the security measures for each way. Also present a security guide and a social network security and privacy made in 2011, and finally a case study about the list of Foreign Terrorist Network dataset.

Social Network Analysis (SNA) is becoming an important tool for investigators, but all the necessary information is often available in a distributed environment. Currently there is no information system that helps managers and team leaders to monitor the status of a social network. This chapter presents an overview of the basic concepts of social networks in data analysis including social networks analysis metrics and performances. Different problems in social networks are discussed such as uncertainty, missing data and finding the shortest path in social network. Community structure, detection and visualization in social network analysis is also discussed and reviewed. This chapter bridges the gap among the users by combining social network analysis methods and information visualization technology to help user visually identify the occurrence of a possible relationship amongst the members in a social network. In addition, briefly describing the different performance measures that have been encountered during any network analysis in order to understand the fundamental behind the comprehension. This chapter also, presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science, which is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. Challenges to be ad dressed and future directions of research are also presented and an extensive bibliography is included.order to understand the fundamental behind the comprehension. This chapter also, presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science, which is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. Challenges to be ad dressed and future directions of research are also presented and an extensive bibliography is included.

This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The first phase is a structure identification, in which unsupervised learning is exploited to determine the RBF centers and widths. This is done by executing different algorithms such as k-mean clustering and standard derivation respectively. The second phase is parameters estimation, in which supervised learning is implemented to establish the connections weights between the hidden layer and the output layer. This is done by performing different algorithms such as Least Mean Squares (LMS) and gradient based methods. The incorporation of PSO in RBF Network hybrid learning is accomplished by optimizing the centers, the widths and the weights of RBF Network. The results for training, testing and validation of five datasets (XOR, Balloon, Cancer, Iris and Ionosphere) illustrates the effectiveness of PSO in enhancing RBF Network learning compared to conventional Backpropogation.

This chapter presents a broad overview of Computational Intelligence (CI) techniques including Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Fuzzy Sets (FS), and Rough Sets (RS). We review a number of applications of computational intelligence to problems in bioinformatics and computational biology, including gene expression, gene selection, cancer classification, protein function prediction, multiple sequence alignment, and DNA fragment assembly. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to solve bioinformatic problems and how bioinformatics could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are presented. An extensive bibliography is also included.

With the widespread and increasing use of internet and digital forms of image; and the convencience of medical professionals that the future of health care will be shaped by teleradiology and technologies such as telemedicine in general. In addition to the various radiological modalities which produce a variety of digital medical files most often datasets and images. These files should be protected from unwanted modification of their contents, especially as they contain vital medical information. Thus their protection and authentication seems to be of great importance and this need will rise along with the future standardization of exchange of data between hospitals or between patients and doctors. In this paper, an associative watermarking scheme is conducted to perform associative watermarking rules to the images which reducts the amount of embedded data, vector quantization indexing scheme is used to embed watermark for the purpose of image authentication. The vector quantization decoding technique is applied to reconstruct the watermarked image from the watermarked index table. The experimental results show that the proposed scheme is robust. The watermarked images are resistant to severe image processing attcks such as Gaussian noise, brightness, blurring, sharpening, cropping, and JPEG lossy compression.

Intrusion detection systems (IDSs) is an essential key for network defense. The hybrid intrusion detection system combines the individual base classifiers and feature selection algorithm to maximize detection accuracy and minimize computational complexity. We investigated the performance of Genetic algorithm-based feature selection system to reduce the data features space and then the hidden naïve bays (HNB) system were adapted to classify the network intrusion into five outcomes: normal, and four anomaly types including denial of service, user-to-root, remote-to-local, and probing. In order to evaluate the performance of introduced hybrid intrusion system, several groups of experiments are conducted and demonstrated on NSL-KDD dataset. Moreover, the performances of intelligent hybrid intrusion system have been compared with the results of well-known feature selection algorithms. It is found that, hybrid intrusion system produces consistently better performances on selecting the subsets of features which resulting better classification accuracies (98.63%).

This paper presents a machine learning (ML) based event detection and summarization system for soccer matches. The proposed system is composed of six phases. Firstly, in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing phase, it applies two types of classification to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the system applies two machine learning algorithms, namely; support vector machine (SVM) and neural network (NN), for emphasizing important segments with logo appearance. Also, in the score board detection phase, the system uses both ML algorithms for detecting the caption region providing information about the score of the game. Subsequently, in the excitement event detection phase, the system uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor filter for detecting goal net. Finally, in the logo-based event detection and summarization phase, the system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. Compared to the performance results obtained using SVM classifier, the proposed system attained good NN-based performance results concerning recall ratio, however it attained poor NN-based performance results concerning precision ratio.